The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.

翻译：随着3D打印等新型制造技术的发展，以往难以实现的化学反应器设计已成为可能。系统性优化这类新型反应器中高度参数化的几何结构，对于确保增强的混合特性及可制造性至关重要。本文提出一种快速求解该非线性、计算成本高且无导数优化问题的框架，支持新颖反应器参数化方案的快速原型设计。我们利用高斯过程在多个连续网格保真度水平上自适应学习反应器模拟的多保真度模型。通过融合不同（可能更低）保真度的模拟结果探索反应器几何搜索空间，这些模拟基于加权采集函数（平衡信息获取与模拟成本）进行选择。在该框架内，我们推导出一种新型准则，用于监控多保真度贝叶斯优化的进度并决定终止时机，确保在实验预算耗尽前返回高保真度解。本研究聚焦于脉冲流条件下的螺旋管式反应器，其具有卓越的混合特性、高度参数化潜力且易于通过3D打印制造。为验证结果，我们通过3D打印制造最优反应器几何构型并开展实验验证其混合性能。由此证明该设计框架可扩展至各类昂贵的基于模拟的优化问题，为下一代高度参数化化学反应器的设计提供支持。